Bibliographic record
Abstract
Although many test case prioritization techniques exist, their performance is far from perfect. Hence, we propose a new fault-based test case prioritization technique to promote fault-revealing test cases inmodel-based testing(MBT) procedures. We seek to improve the fault detection rate—a measure of how fast a test suite is able to detect faults during testing—in scenarios such as regression testing. We propose an extended digraph model as the basis of this new technique. The model is realized using a novelreinforcement-learning(RL)- and hidden-Markov-model (HMM)-based technique which is able to prioritize test cases for regression testing objectives. We present a method to initialize and train an HMM based upon RL concepts applied to an application's digraph model. The model prioritizes test cases based upon forward probabilities, a new test case prioritization approach. In addition, we also propose an alternative approach to prioritizing test cases according to the amount of change they cause in applications. To evaluate the effectiveness of the proposed techniques, we perform experiments ongraphical user interface(GUI)-based applications and compare the results with state-of-the-art test case prioritization approaches. The experimental results show that the proposed technique is able to detect faults early within test runs.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".